<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Train Model from PDFs</title>
<a href="entrenament-pdf.html" style="margin:5px;padding: 5px; border:1px solid green">Entrenament PDF</a>
<a href="preguntar-pdf.html" style="margin:5px;padding: 5px; border:1px solid green">Preguntar PDF</a>
<br><br>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
<script src="https://cdn.jsdelivr.net/npm/pdfjs-dist/build/pdf.min.js"></script>
</head>
<body>
<h1>Train Model from PDFs</h1>
<input type="file" id="fileInput" multiple>
<button id="trainModel">Train Model</button>
<pre id="status"></pre>
<script>
async function extractTextFromPDF(file) {
const pdf = await pdfjsLib.getDocument(await file.arrayBuffer()).promise;
let text = '';
for (let i = 1; i <= pdf.numPages; i++) {
const page = await pdf.getPage(i);
const content = await page.getTextContent();
text += content.items.map(item => item.str).join(' ') + ' ';
}
return text;
}
async function trainModel(data) {
const model = tf.sequential();
model.add(tf.layers.dense({
units: 128,
activation: 'relu',
inputShape: [data[0].length]
}));
model.add(tf.layers.dense({ units: 64, activation: 'relu' }));
model.add(tf.layers.dense({ units: 1, activation: 'sigmoid' }));
model.compile({
optimizer: 'adam',
loss: 'binaryCrossentropy',
metrics: ['accuracy']
});
const inputs = tf.tensor2d(data.map(d => d.input));
const labels = tf.tensor1d(data.map(d => d.label));
document.getElementById('status').textContent = 'Training the model...';
await model.fit(inputs, labels, {
epochs: 10,
callbacks: {
onEpochEnd: (epoch, logs) => {
console.log(`Epoch ${epoch}: loss = ${logs.loss}`);
document.getElementById('status').textContent = `Epoch ${epoch + 1}: Loss = ${logs.loss}`;
}
}
});
document.getElementById('status').textContent = 'Saving the model to IndexedDB...';
try {
await model.save('indexeddb://pdf-trained-model');
document.getElementById('status').textContent = 'Model saved successfully in IndexedDB!';
} catch (err) {
document.getElementById('status').textContent = 'Error saving the model: ' + err.message;
console.error('Error saving the model:', err);
}
}
document.getElementById('trainModel').addEventListener('click', async () => {
const files = document.getElementById('fileInput').files;
if (!files.length) {
document.getElementById('status').textContent = 'Please select PDF files to train the model.';
return;
}
const data = [];
document.getElementById('status').textContent = 'Extracting text from PDFs...';
for (const file of files) {
const text = await extractTextFromPDF(file);
const tokens = text.split(/\s+/).map(word => word.length); // Example: using word lengths as features
data.push({
input: tokens.slice(0, 10), // Use the first 10 tokens as input features
label: 1 // Example label (adjust as needed for your use case)
});
}
document.getElementById('status').textContent = 'Training the model...';
await trainModel(data);
});
</script>
</body>
</html>